Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 17; no. 8; pp. 2367 - 2380
Main Authors: Kim, Gitae, Ong, Yew Soon, Cheong, Taesu, Tan, Puay Siew
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
approximate dynamic programming
Dynamic programming
Dynamic vehicle routing problem
Dynamics
Markov models
Roads
rollout algorithm
Route selection
Stochastic processes
Traffic congestion
Traffic flow
Traffic models
Travel time
uncertain travel times
Urban areas
Vehicle dynamics
Vehicle routing
Vehicles
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2521779